Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility. This is a 9B parameter dense model, supporting a native context length of 262,144 tokens.

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Capabilities

Vision Input
Reasoning

Minimum system memory

7GB

Tags

9B
qwen35

Last updated

Updated 1 day agoby
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lmmy

README

Qwen3.5 9B

Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility. This is a 9B parameter dense model, supporting a native context length of 262,144 tokens.

Highlights

Unified Vision-Language Foundation. Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.

Scalable RL Generalization. Reinforcement learning scaled across million-agent environments with progressively complex task distributions for robust real-world adaptability.

Global Linguistic Coverage. Expanded support to 201 languages and dialects, enabling inclusive, worldwide deployment with nuanced cultural and regional understanding.

Custom Fields

Special features defined by the model author

Enable Thinking

: boolean

(default=true)

Controls whether the model will think before replying

Parameters

Custom configuration options included with this model

Min P Sampling
Disabled
Repeat Penalty
Disabled
Temperature
1
Top K Sampling
20
Top P Sampling
0.95

Sources

The underlying model files this model uses